According to the World Health Organization (WHO), the world has been battling the spread of the coronavirus disease of 2019 or the COVID-19 pandemic since March 2020, with over 118,319 positive cases and 4292 deaths globally. Surabaya, the capital of East Java, has the second largest population in Indonesia with 2,874,314 people and it is the second largest city after Jakarta. It is also included as the largest metropolitan area in eastern Indonesia, which known as Gerbangkertosusila (Gresik–Bangkalan–Mojokerto–Surabaya–Sidoarjo-Lamongan). According to the Satgas of East Java, the city of Surabaya had the highest number of deaths in the province, with a total of 1382 deaths.
A regional spatiotemporal understanding of COVID-19 is critical to provide insight into how the pandemic occurred and its continued growth and decline. The virus spread can be caused by several factors, asides from the spatial variable. A study by Hassan et al, demonstrated the spatial relationships between COVID-19 cases and several variables, such as air pollution, geo-meteorological, and social parameters. The study found a significant robust relationship between those variables in cities with significant positive cases. In addition, the correlation finding suggests that long-term bad air quality may aggravate the clinical symptoms of the disease.
However, no research has been conducted on the concurrent correlation between spatial, temporal, and air pollution. Therefore, this study aims to (1) assess the spatiotemporal pattern of COVID-19 using updated daily data recorded for 26 weeks, and (2) investigate various factors, such as confirmed cases, recovered patients, suspected cases, and air pollution (CO, SO2, O3, and NO parameters) by conducting geographically weighted regression (GWR) and ordinary least squares (OLS) regression. It aims to (3) analyze the relationship between air pollution concentrations and COVID-19 cases for 26 weeks, estimated by Sentinel-5P imagery. This research focused on the Surabaya city as one of the areas with a high death rate during the delta variant period, according to a WHO report on 23 June 2021. Therefore, the result is expected to help the Surabaya government create more appropriate policies and strategies to reduce the virus spread.
The COVID-19 data obtained in Surabaya from 28 April to 26 October 2021 used the spatial CGD, Moran’s I, General-G, Gi* statistics, and the GWR models to conclude that the delta variant has a significant spatial correlation with the variables. Although Moran’s global I and G statistics were used to identify strong spatial patterns of the virus regarding the variables, this approach only considers single-layer distributions at any given time. This study was able to identify which village demonstrated a high probability of infection using Gi* hotspot and cold spot analysis. Therefore, future studies need to investigate other correlations, such as ecological, climatological, and socioeconomic variables, to effectively determine the relationship between COVID-19 hotspots, cold spots, and population density. Although the transmission is currently showing a declining trend, the epidemic situation in eastern and southern Surabaya is difficult. The spatiotemporal analysis demonstrated in this study suggests that a temporal hazard model based on weekly infection rates led to a better understanding of changes. The pollutant processing results with the number of confirmed cases of COVID-19, especially the delta variant, demonstrated a fairly strong positive correlation value of 0.577 in the third week, which was the highest. Therefore, COVID-19 is correlated with pollution levels. This study is expected to provide a useful strategy in improving the infectious disease surveillance system and control intervention in each affected area for future purposes. GIS was used to map disease incidence against several parameters, including demographics, environment, geography, and past events, to understand outbreak origins, spread patterns, and intensity, supporting the implementation of control, preventive, and surveillance measures.
There are several caveats and inherent limitations in this study. First, Sentinel-5P has a low geographical resolution compared to village areas, demanding the usage of medium resolution satellite photography. Sentinel-5, on the other hand, has a unique mission and sensor for monitoring the troposphere on a city-scale, allowing the researcher to collect data on O3, CO, SO2, and NO2. Due to the highly dynamic nature of the disease, such as the highly contagious delta variant, it is necessary to add an analysis of the vaccination rate in each region to reduce the number of distributions and positive cases. For future studies, adding vaccination variables for each region can be added to see the rate of infection and the spread of COVID 19.
Author: Dr. Christrijogo Soemartono W, dr., SpAn., KAR., KIC
Detailed information from this research can be viewed on our article at:
https://www.mdpi.com/1660-4601/19/3/1614
Cahyadi MN, Handayani HH, Warmadewanthi IDAA, Rokhmana CA, Sulistiawan SS, Waloedjo CS, et al. Spatiotemporal Analysis for COVID-19 Delta Variant Using GIS-Based Air Parameter and Spatial Modeling. Int J Environ Res Public Health [Internet]. 2022;19(3):1–21. Available from: https://www.mdpi.com/1660-4601/19/3/1614
